In parallel to the deep learning improvements that are mostly driven by extracting more useful information from the waveform, researchers have made progress in obtaining a better understanding of the biases and factors of listening tests that are independent of the speech signal being rated [1]}. For example, research has found that a significant amount of bias may be attributable to properties of the listeners, including their language and culture, as well as their individual tendencies to rate high or low [2]}, [3]}. LDNet [4]}, a baseline model for this challenge, considers rater metadata.
